Citation

Tee, Connie and Goh, Michael Kah Ong and Teoh, Andrew Beng Jin (2013) Gait recognition using Sparse Grassmannian Locality Preserving Discriminant Analysis. 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). pp. 2989-2993. ISSN 1520-6149

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Abstract

One of the greatest challenges for gait recognition is identification across appearance change. In this paper, we present a gait recognition method called Sparse Grassmannian Locality Preserving Discriminant Analysis. The proposed method learns a compact and rich representation of the gait images through sparse representation. The use of Grassmannian locality preserving discriminant analysis further optimizes the performance by preserving both global discriminant and local geometrical structure of the gait data. Experiments demonstrate that the proposed method can tolerate variation in appearance for gait identification effectively.

Item Type:	Article
Subjects:	T Technology > T Technology (General)
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	20 Mar 2014 09:35
Last Modified:	20 Mar 2014 09:35
URII:	http://shdl.mmu.edu.my/id/eprint/5397

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